Automated software configuration management

ABSTRACT

A system uses agents to monitor a distributed business transaction as well as monitor changes in software configuration. An agent may detect a file load, such a class load, obtain portions of the file such as functions, and perform a hash on the byte code functions. A hash tree may then be constructed and compared to previous states of the system. The hash tree may be generated, for example, at each file loading detected, so that system states can be compared. Differences in hash trees are detected and the changes are reported to an administrator of the system that provides the distrusted business transaction.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/611,024, titled “AUTOMATED SOFTWARE CONFIGURATION MANAGEMENT,” filedJan. 30, 2015, the disclosure of which is incorporated herein byreference.

BACKGROUND

The World Wide Web has expanded to provide web services faster toconsumers. Web services may be provided by a web application which usesone or more services to handle a transaction. The applications may bedistributed over several machines, making the topology of the machinesthat provides the service more difficult to track and monitor.

Managing configuration of software that provides a distributed businesstransaction can be cumbersome. When considering computer systems andsoftware applications in a large data center, ensuring that thesesystems are property configured is a serious challenge. With the recenttrends towards micro service architectures, the complexities ofconfiguration have only gotten more difficult, as it becomes easier andeasier to misconfigure not just an individual node, but to misconfigurethe system topology.

In addition, with recent trends towards agile software development withfrequent (sometimes even multiple times per day) releases of new code,ensuring that the correct version of software is deployed on the correctcomputer systems becomes its own configuration challenge. This can getmore and more complicated as multiple independent teams in the softwaredevelopment organization collaborate on releasing software that maysometimes be packaged together in one large deployment binary. Thechallenge of ensuring that the correct version of software has beendeployed can be quite difficult when considering such a scenario as mosttools allow a granularity of a deployment binary, and cannot lookinside.

Furthermore, in modern architectures, modern systems call out to a cloudservice to discover their configuration. It therefore becomes achallenge to understand the actual running configuration as opposed towhat is statically visible on the file system.

There is a need in the art for providing improved software configurationmanagement.

SUMMARY OF THE CLAIMED INVENTION

The present technology uses agents to monitor a distributed businesstransaction as well as monitor changes in software configuration. Anagent may detect a file load, such a class load, obtain portions of thefile such as functions, and perform a hash on the byte code functions. Ahash tree may then be constructed and compared to previous states of thesystem. The hash tree may be generated, for example, at each fileloading detected, so that system states can be compared. Differences inhash trees are detected and the changes are reported to an administratorof the system that provides the distrusted business transaction.

An embodiment may include a method for managing software configurations.The method may include detecting a file load in an application by anagent installed on the application, wherein the application one of aplurality of applications that provide a distributed businesstransaction.

The components of the file may be identified by the agent. A hash of thefile components may be performed by the agent. A structured collectionof the hash values may be compared to a previously constructedcollection of the hash values. The results may be reported of thecomparison.

An embodiment may include a system for monitoring a businesstransaction. The system may include a processor, memory and one or moremodules stored in memory and executable by the processor. When executed,the one or more modules may detect a file load in an application by anagent installed on the application, the application one of a pluralityof applications that provide a distributed business transaction,identify the components of the file by the agent, perform a hash of thefile components by the agent, compare a structured collection of thehash values to a previously constructed collection of the hash valuesand report the results of the comparison.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an exemplary system for monitoring adistributed application.

FIG. 2 is a block diagram of an agent.

FIG. 3 is a method for determining a change in a software system.

FIG. 4 is a method for preparing a hash tree.

FIG. 5 is a block diagram of an exemplary system for implementing acomputing device.

DETAILED DESCRIPTION

The present technology uses agents to monitor a distributed businesstransaction as well as monitor changes in software configuration. Anagent may detect a file load, such a class load, obtain portions of thefile such as functions, and perform a hash on the byte code functions. Ahash tree may then be constructed and compared to previous states of thesystem. The hash tree may be generated, for example, at each fileloading detected, so that system states can be compared. Differences inhash trees are detected and the changes are reported to an administratorof the system that provides the distrusted business transaction.

Content is addressed by a checksum of itself, regardless of location inthe filesystem hierarchy, thus allowing for an extremely efficientrepresentation of similar content existing in different locations. Thepresent system can model a data center configuration as such afilesystem, getting as granular as decompiled programming primitivessuch as classes, functions, and so on within binary code such as Javabytecode.

This approach solves two of the largest challenges in performingdistributed configuration management: in a large datacenter,representing the configuration within the configuration managementsystem can be an exercise in futility, as there is a lot of it. Having away to store the same configuration once while understanding where it ispresent in a distributed manner allows the present configurationmanagement system to use minimal storage. Furthermore, the presentsystem can track changes over space and time in an extremely efficientway, as it can quickly tell if the same configuration on two differentnodes has the same checksum, and only if not copy deltas around to thecentral management system. This approach allows for a highly scalable(in resources and speed) configuration management system that can trackchange in real time.

Additionally, this approach allows tracking change to blocks of codeinside a binary program. We can easily tell if the same class binary hasbeen deployed into an EAR file across many application servers withoutcaring specifically if the EAR files are holistically the same. This isaccomplished by looking inside the EAR files and tracking checksums ofeach class file independently. The system can get even get more specificby comparing methods inside the same class file, using the same checksumtechnique.

EAR files are a Java specific format and just one example of a servicedeployment artifact. References to EAR files are only used as an exampleherein. The techniques should be applicable to a variety of generalservice deployment artifacts, e.g: WAR files, python packages, .NET WebDeployment Packages, docker apps, and so forth.

We take our solution one step further by attempting to trackconfiguration at runtime, as opposed to merely tracking configuration aspersisted on the filesystems of the computer systems that we aremanaging. We do this by embedding our configuration management softwareinside of an agent that is instrumenting the running software inprocess. This allows us not just to see which class files are sittinginside of an EAR file, but which class files have actually been loadedinto memory and are executing. Similarly, being inside the processallows us to see the actual configuration parameters that are being usedto configure the running program, including those that have dynamicallybeen discovered, for example by calling out to a cloud service. FIG. 1is a block diagram of a system for monitoring a distributed application.System 100 of FIG. 1 includes client device 105 and 192, mobile device115, network 120, network server 125, application servers 130, 140, 150and 160, asynchronous network machine 170, data stores 180 and 185, andcontroller 190.

Client device 105 may include network browser 110 and be implemented asa computing device, such as for example a laptop, desktop, workstation,or some other computing device. Network browser 110 may be a clientapplication for viewing content provided by an application server, suchas application server 130 via network server 125 over network 120.Mobile device 115 is connected to network 120 and may be implemented asa portable device suitable for receiving content over a network, such asfor example a mobile phone, smart phone, tablet computer or otherportable device. Both client device 105 and mobile device 115 mayinclude hardware and/or software configured to access a web serviceprovided by network server 125.

Network 120 may facilitate communication of data between differentservers, devices and machines. The network may be implemented as aprivate network, public network, intranet, the Internet, a Wi-Finetwork, cellular network, or a combination of these networks.

Network server 125 is connected to network 120 and may receive andprocess requests received over network 120. Network server 125 may beimplemented as one or more servers implementing a network service. Whennetwork 120 is the Internet, network server 125 may be implemented as aweb server. Network server 125 and application server 130 may beimplemented on separate or the same server or machine.

Application server 130 communicates with network server 125, applicationservers 140 and 150, controller 190. Application server 130 may alsocommunicate with other machines and devices (not illustrated in FIG. 1).Application server 130 may host an application or portions of adistributed application and include a virtual machine 132, agent 134,and other software modules. Application server 130 may be implemented asone server or multiple servers as illustrated in FIG. 1, and mayimplement both an application server and network server on a singlemachine.

Application server 130 may include applications in one or more ofseveral platforms. For example, application server 130 may include aJava application, .NET application, PHP application, C++ application,AJAX, or other application. Different platforms are discussed below forpurposes of example only.

Virtual machine 132 may be implemented by code running on one or moreapplication servers. The code may implement computer programs, modulesand data structures to implement, for example, a virtual machine modefor executing programs and applications. In some embodiments, more thanone virtual machine 132 may execute on an application server 130. Avirtual machine may be implemented as a Java Virtual Machine (JVM).Virtual machine 132 may perform all or a portion of a businesstransaction performed by application servers comprising system 100. Avirtual machine may be considered one of several services that implementa web service.

Virtual machine 132 may be instrumented using byte code insertion, orbyte code instrumentation, to modify the object code of the virtualmachine. The instrumented object code may include code used to detectcalls received by virtual machine 132, calls sent by virtual machine132, and communicate with agent 134 during execution of an applicationon virtual machine 132. Alternatively, other code may be byte codeinstrumented, such as code comprising an application which executeswithin virtual machine 132 or an application which may be executed onapplication server 130 and outside virtual machine 132.

Agent 134 on application server 130 may be installed on applicationserver 130 by instrumentation of object code, downloading theapplication to the server, or in some other manner. Agent 134 may beexecuted to monitor application server 130, monitor virtual machine 132,and communicate with byte instrumented code on application server 130,virtual machine 132 or another application or program on applicationserver 130. Agent 134 may detect operations such as receiving calls andsending requests by application server 130 and virtual machine 132.Agent 134 may receive data from instrumented code of the virtual machine132, process the data and transmit the data to controller 190. Agent 134may perform other operations related to monitoring virtual machine 132and application server 130 as discussed herein. For example, agent 134may identify other applications, share business transaction data,aggregate detected runtime data, and other operations.

Agent 134 may be a Java agent, .NET agent, PHP agent, or some other typeof agent, for example based on the platform which the agent is installedon.

Each of application servers 140, 150 and 160 may include an applicationand an agent. Each application may run on the corresponding applicationserver or a virtual machine. Each of virtual machines 142, 152 and 162on application servers 140-160 may operate similarly to virtual machine132 and host one or more applications which perform at least a portionof a distributed business transaction. Agents 144, 154 and 164 maymonitor the virtual machines 142-162 or other software processingrequests, collect and process data at runtime of the virtual machines,and communicate with controller 190. The virtual machines 132, 142, 152and 162 may communicate with each other as part of performing adistributed transaction. In particular each virtual machine may call anyapplication or method of another virtual machine.

Asynchronous network machine 170 may engage in asynchronouscommunications with one or more application servers, such as applicationserver 150 and 160. For example, application server 150 may transmitseveral calls or messages to an asynchronous network machine. Ratherthan communicate back to application server 150, the asynchronousnetwork machine may process the messages and eventually provide aresponse, such as a processed message, to application server 160.Because there is no return message from the asynchronous network machineto application server 150, the communications between them areasynchronous.

Data stores 180 and 185 may each be accessed by application servers suchas application server 150. Data store 185 may also be accessed byapplication server 150. Each of data stores 180 and 185 may store data,process data, and return queries received from an application server.Each of data stores 180 and 185 may or may not include an agent.

Controller 190 may control and manage monitoring of businesstransactions distributed over application servers 130-160. Controller190 may receive runtime data from each of agents 134-164, associateportions of business transaction data, communicate with agents toconfigure collection of runtime data, and provide performance data andreporting through an interface. The interface may be viewed as aweb-based interface viewable by mobile device 115, client device 105, orsome other device. In some embodiments, a client device 192 may directlycommunicate with controller 190 to view an interface for monitoringdata.

Controller 190 may install an agent into one or more virtual machinesand/or application servers 130. Controller 190 may receive correlationconfiguration data, such as an object, a method, or class identifier,from a user through client device 192.

Controller 190 may collect and monitor customer usage data collected byagents on customer application servers and analyze the data. Thecontroller may report the analyzed data via one or more interfaces,including but not limited to a dashboard interface and one or morereports.

Data collection server 195 may communicate with client 105, 115 (notshown in FIG. 1), and controller 190, as well as other machines in thesystem of FIG. 1. Data collection server 195 may receive data associatedwith monitoring a client request at client 105 (or mobile device 115)and may store and aggregate the data. The stored and/or aggregated datamay be provided to controller 190 for reporting to a user.

FIG. 2 is a block diagram of an agent. Agent 200 may include hash engine210 and tree building engine 220. The hash engine may perform a hash offunction level elements in a software environment. The hash may beperformed on byte code of the particular functions, configuration files,and other files. The hash function may be any suitable function fordetermining a number from the byte code.

The tree building engine 220 may be build a tree such as a Merkle treefrom the hash values. The tree building engine may also compare Merkletree of hash values generated at different times.

FIG. 3 is a method for determining a change in a software system. Adistributed business transaction may be monitored at step 310. Themonitoring may involve agents installed on applications, virtualmachines, and other components that implement a distributed businesstransaction, and collection of data regarding the execution of adistributed business transaction by the agents.

An agent may detect the loading of a file into the system, for exampleinto a JVM, at step 320. The file may be a class containing functionsand data, a configuration file, an XML file, or other file.

A hash tree is prepared for the loaded file at step 330. Preparing thehash tree may include identifying parts of the file, performing a hashon one or more of the parts, and building the hash tree from the hashedparts of the file. Preparing a hash tree is discussed in more detailbelow with respect to the method of FIG. 4.

The current hash tree (prepared at step 330) may be compared to aprevious hash tree at step 340. The comparison may be preceded by aquery by the agent to determine if a previous hash tree is available tocompare. The comparison would then proceed if a suitable hash tree wasavailable.

In any case, the result of the comparison may indicate a change betweenthe hash trees, but it would not indicate exactly what the difference isbecause the hashes are in byte code. Hence, the difference in byte codewould still be in byte code, and would not be human readable. Though thedetails of the difference would not be immediately recognizable, thehash and hash tree comparison could be completed very quickly in binaryformat.

In some instances, the comparison of hash trees is done at thecontroller. In this case, the agent would perform hashes, build the hashtree, and transmit the hash tree, for example a Merkle tree, to thecontroller. The controller may then compare the hash trees received fromall agents to a previous version received from all agents.

A difference in the hash trees is determined in response to thecomparison at step 350. The difference may have a granularity down tothe function level for the application, JVM, or whatever is beingcompared. The differences may be stored and reported to an administratorat step 360.

FIG. 4 is a method for preparing a hash tree. The method of FIG. 4provides more detail for step 330 of the method of FIG. 3. First, thecompiled file, such as a class or configuration file, is retrieved atstep 410. The file data and functions are determined from the retrievedcompiled file at step 420. The agent may utilize an Java API todetermine the functions and data that make up the loaded class toprocess.

Once the functions or file parts are known, a hash can be performed onthe functions at step 430. The hash function may be any hash that issuitable for generating a hash value from which a hash tree, such as aMerkle tree, can be constructed.

FIG. 5 illustrates an exemplary computing system 500 that may be used toimplement a computing device for use with the present technology. System500 of FIG. 5 may be implemented in the contexts of the likes of clients55-115, network server 125, application servers 130-160, machine 170,data stores 180-185, and controller 190. The computing system 500 ofFIG. 5 includes one or more processors 54 and memory 54. Main memory 54stores, in part, instructions and data for execution by processor 54.Main memory 54 can store the executable code when in operation. Thesystem 500 of FIG. 5 further includes a mass storage device 530,portable storage medium drive(s) 540, output devices 550, user inputdevices 560, a graphics display 570, and peripheral devices 580.

The components shown in FIG. 5 are depicted as being connected via asingle bus 590. However, the components may be connected through one ormore data transport means. For example, processor unit 54 and mainmemory 54 may be connected via a local microprocessor bus, and the massstorage device 530, peripheral device(s) 580, portable storage device540, and display system 570 may be connected via one or moreinput/output (I/O) buses.

Mass storage device 530, which may be implemented with a magnetic diskdrive or an optical disk drive, is a non-volatile storage device forstoring data and instructions for use by processor unit 54. Mass storagedevice 530 can store the system software for implementing embodiments ofthe present invention for purposes of loading that software into mainmemory 54.

Portable storage device 540 operates in conjunction with a portablenon-volatile storage medium, such as a floppy disk, compact disk orDigital video disc, to input and output data and code to and from thecomputer system 500 of FIG. 5. The system software for implementingembodiments of the present invention may be stored on such a portablemedium and input to the computer system 500 via the portable storagedevice 540.

Input devices 560 provide a portion of a user interface. Input devices560 may include an alpha-numeric keypad, such as a keyboard, forinputting alpha-numeric and other information, or a pointing device,such as a mouse, a trackball, stylus, or cursor direction keys.Additionally, the system 500 as shown in FIG. 5 includes output devices550. Examples of suitable output devices include speakers, printers,network interfaces, and monitors.

Display system 570 may include a liquid crystal display (LCD) or othersuitable display device. Display system 570 receives textual andgraphical information, and processes the information for output to thedisplay device.

Peripherals 580 may include any type of computer support device to addadditional functionality to the computer system. For example, peripheraldevice(s) 580 may include a modem or a router.

The components contained in the computer system 500 of FIG. 5 are thosetypically found in computer systems that may be suitable for use withembodiments of the present invention and are intended to represent abroad category of such computer components that are well known in theart. Thus, the computer system 500 of FIG. 5 can be a personal computer,hand held computing device, telephone, mobile computing device,workstation, server, minicomputer, mainframe computer, or any othercomputing device. The computer can also include different busconfigurations, networked platforms, multi-processor platforms, etc.Various operating systems can be used including Unix, Linux, Windows,Macintosh OS, Palm OS, and other suitable operating systems.

The foregoing detailed description of the technology herein has beenpresented for purposes of illustration and description. It is notintended to be exhaustive or to limit the technology to the precise formdisclosed. Many modifications and variations are possible in light ofthe above teaching. The described embodiments were chosen in order tobest explain the principles of the technology and its practicalapplication to thereby enable others skilled in the art to best utilizethe technology in various embodiments and with various modifications asare suited to the particular use contemplated. It is intended that thescope of the technology be defined by the claims appended hereto.

1. (canceled)
 2. A method, comprising: detecting, by an agent atruntime, loading of a file in an application, the application being oneof a plurality of applications that provide a distributed businesstransaction; responsive to the detecting, identifying, by the agent,parts of the loaded file; performing, by the agent, a hash of the partsof the loaded file to generate corresponding hash values; constructing,by the agent, a hash tree from the generated hash values; determining,by the agent, whether a previously constructed hash tree from apreviously detected load of the file is available to perform acomparison; comparing, by the agent, the constructed hash tree againstthe previously constructed hash tree to identify changes to blocks ofcode inside the loaded file, wherein the identified changes indicate achange in the distributed business transaction; and reporting, by theagent, results of the comparison.
 3. The method of claim 2, wherein theparts include functions and data, and wherein the hash is performed onthe functions.
 4. The method of claim 2, wherein the parts includeportions of a configuration file.
 5. The method of claim 2, wherein thehash tree is a Merkle tree.
 6. The method of claim 2, wherein the agentsconstructs a new hash tree in response to detection of each loading of agiven file.
 7. A non-transitory computer-readable storage medium havingembodied thereon a program, the program being executable by a processorto perform operations for managing software configuration, theoperations including: detecting, as an agent at runtime, loading of afile in an application, the application being one of a plurality ofapplications that provide a distributed business transaction; responsiveto the detecting, identifying parts of the loaded file; performing ahash of the parts of the loaded file to generate corresponding hashvalues; constructing a hash tree from the generated hash values;determining whether a previously constructed hash tree from a previouslydetected load of the file is available to perform a comparison;comparing the constructed hash tree against the previously constructedhash tree to identify changes to blocks of code inside the loaded file,wherein the identified changes indicate a change in the distributedbusiness transaction; and reporting results of the comparison.
 8. Thenon-transitory computer-readable storage medium of claim 7, wherein theparts include functions and data, and wherein the hash is performed onthe functions.
 9. The non-transitory computer-readable storage medium ofclaim 7, wherein the parts include portions of a configuration file. 10.The non-transitory computer-readable storage medium of claim 7, whereinthe agents constructs a new hash tree in response to detection of eachloading of a given file.
 11. The non-transitory computer-readablestorage medium of claim 7, wherein the hash tree is a Merkle tree.
 12. Amethod, comprising: receiving, at a controller from an agent executingon a server in a network, a hash tree constructed by the agent, whereinthe hash tree is constructed from hash values obtained by performing ahash on parts of a file in an application detected by the agent duringloading of the application, the application one of a plurality ofapplications that provide a distributed business transaction in thenetwork; determining, by the controller, whether a previouslyconstructed hash tree from a previously detected load of the file isavailable to perform a comparison with the received hash tree;comparing, by the controller, the received hash tree against thepreviously constructed hash tree to identify changes to blocks of codeinside the detected file, wherein the identified changes indicate achange in the distributed business transaction; and reporting, by thecontroller, results of the comparison.
 13. The method of claim 12,wherein the parts of the file include functions and data, and whereinthe hash is performed on the functions in the file.
 14. The method ofclaim 12, wherein the parts include portions of a configuration file.15. The method of claim 12, wherein the agents constructs a new hashtree in response to detection of each loading of a given file.
 16. Themethod of claim 12, wherein the hash tree is a Merkle tree.
 17. Themethod of claim 12, wherein comparing includes comparing each hash treereceived from a plurality of agents to a previous version received fromeach of the plurality of agents.
 18. The method of claim 12, whereincomparing identifies differences in byte code functions of the file. 19.The method of claim 12, further comprising: tracking changes to thedistributed business transaction over time.
 20. The method of claim 12,wherein comparing further determining whether a same class binary hasbeen deployed into like files across a plurality of application servers.21. The method of claim 12, wherein the file is an EnterpriseApplication aRchive (EAR) file.